Curated GitHub repositories every AI agent should know.
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OpenAI's GPT-6.1 Sol and Anthropic's Claude Sonnet 5.5 both launched at $2/$10 within 24 hours. Identical sticker price means the decision moves to harness, cache economics, and tokens-per-task — here's how to actually pick.
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Context engineering is curating the exact tokens Claude sees at inference. Skills are the cleanest tool for it: they keep only a one-line trigger in context and load the full instructions on demand. Here's the SKILL.md syntax and the workflow.
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The one formula that turns per-token prices into a monthly number, worked examples on verified September-2026 rates, the word-to-token conversion you need to use it, and a free interactive calculator to plug in your own workload.
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Claude Code runs three ways inside VS Code — a graphical panel, the integrated terminal, or an external terminal bridged with /ide. Here's how to install it in 60 seconds, sign in without an API key, and the settings and shortcuts that make it worth keeping open.
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Stop sending everything to a flagship. Classify each request by difficulty, route it to the cheapest model that clears the bar, and measure cost per finished job.
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Five general-purpose agents now browse, click, fill forms and hand back finished work — not just chat. Here's which one to run for research, everyday web tasks, or full autonomy, what real agent power costs, and the one risk that should keep you out of your bank account.
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Most 'best open-source coder' lists rank models you can't run: GLM-5.3 is 744B, DeepSeek V3.2 is 685B, Kimi K3 is 2.8T. On hardware a solo founder owns, the real choice is narrow — and it's a Qwen. Here's what fits one GPU, what fits two, and what you should just rent an API for.
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The fastest path from a fresh VS Code install to Claude editing your repo — the exact install commands, how to open the panel, and the five keyboard moves that make it feel native instead of bolted on.
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Vibe coding means describing what you want in plain language and shipping whatever the AI produces without reading the code. Here's the precise definition, the apps a solo founder actually reaches for in 2026 — Lovable, Bolt, v0, Cursor, Claude Code, Replit — and the exact point where it stops working.
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OpenAI's Sponsored Agents split AI search into a paid lane and an organic lane, the same way Google did in 2002. Here's exactly what changed on Sept 16, what it does and doesn't cost you, and the concrete playbook to keep showing up in the free answer before the paid lane crowds it.
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What serverless GPU compute actually is, the September 2026 price table for the providers that offer it — Modal, RunPod, Replicate, Beam and Baseten — and the one number (your utilization) that decides whether it's cheaper than renting a dedicated GPU. Plus the cold-start tax nobody quotes you up front.
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The real roles, who's actually hiring, what the numbers say about pay, and the lateral path in from appsec, pentesting, or ML engineering — no PhD required.
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The models topping the open-weight leaderboards are trillion-parameter giants you can't run at home. The ones you can run on a single 24GB GPU are a different, shorter list — and the license, not the benchmark, decides which you can put in a product.
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Install the Anthropic extension, open a file, click the Spark icon, and sign in with a paid Claude account — the panel bundles its own CLI, so there's nothing else to set up.
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There are two honest answers to 'how do I build an AI agent with ChatGPT' — a no-code one inside ChatGPT and a code one with the OpenAI Agents SDK. Here's how to pick, and a working Python agent you can run today.
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OpenAI just made the managed Codex harness a buy decision. Here's the honest build-vs-buy for a solo founder — what each option runs for you, what it costs, and where the lock-in hides — with a one-line rule for picking.
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The phrase 'serverless GPU' hides two different products, and picking the wrong one is the most expensive mistake in this category. Here's the scale-to-zero test, a price-and-cold-start comparison you can act on, and the one platform that fits each founder situation.
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An updated per-token price table for the models founders actually ship on — now with GPT-6 Astra at the top and Fable 5.1's 75%-cheaper cache reads — plus the one formula that turns those numbers into a monthly bill, and the Jan 1 promo cliff you have to price your 2027 into today.
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The best open-weight coder to self-host is a hardware question, not a leaderboard question — here's the ranking and the VRAM to run each.
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Nine concrete practices you can act on today to keep an autonomous agent from leaking your secrets, over-spending your money, or getting talked into doing something dumb.
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An MCP server is a small program that exposes your tools and data to an AI model in a standard way — so any AI client can use them without custom glue. Here's the plain-English definition, how it differs from a REST API, and when you actually need one.
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The 'best LLM for image generation' is really an image model, and the right one depends on the job: GPT Image 2 for top quality, Nano Banana 2 for the best value, FLUX.2 if you need open weights. Here's the pick-by-use-case, the real per-image prices, and which one to put in your product.
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The specialty-vs-hyperscaler spread is still ~5–7× for the identical card. What changed this month: the Blackwell B200 floor cracked below $4/hr, Grace-Blackwell superchips now rent by the hour, and — the twist — AWS actually RAISED its prices while the neoclouds kept cutting. Here's the September on-demand map and the three numbers that decide which column you belong in.
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GraphRAG's price isn't hidden in the query — it's front-loaded into indexing, where an LLM reads every chunk of your corpus to build the graph. Here's where the money actually goes, why Microsoft shipped a variant that indexes for ~0.1% of the cost, and a decision framework for capping each line before you turn it on.
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The fastest way to give Claude, Copilot, or Cursor real access to your repos, issues, and PRs is the official github/github-mcp-server — a hosted endpoint you point your agent at. Here's the exact config for each client, how to scope it so an agent can't do more than you meant, and when you'd build your own MCP server instead.
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Install Ollama, run one command, and you have a private LLM on your own machine in about five minutes. Here is the fast path, how to pick a model for your GPU, and how to expose it as an OpenAI-compatible endpoint your code already knows how to call.
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You want 16GB of VRAM to run local coding models as cheaply as possible. The 2026 memory crunch roughly doubled the obvious pick — here's the card that's actually cheapest, and the used one that quietly beats them all.
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You want a coding model that runs on your laptop — private, free per token, works offline. Here's the one to install for your exact hardware, the VRAM math, and the tools that wire it into your editor.
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A working map of agent memory as it actually stands in 2026 — the short-term/long-term split, the episodic/semantic/procedural types, and the seven systems founders actually reach for: Mem0, Zep/Graphiti, Letta, LangMem, Cognee, Redis, and Google's Vertex Memory Bank. Includes the one thing every vendor benchmark gets wrong, and a decision tree you can use this afternoon.
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One benchmark now ranks 47 models on prose quality, and the answer is clearer than the marketing suggests: Claude Opus 5 writes best, Claude Sonnet 5 is the value pick, and GLM-5.3 leads the open-weight field. Here's which to reach for by the job you're actually doing — long-form drafts, marketing copy, docs, or editing — and when a cheaper model is the right call.
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